Pd qPCR Interlaboratory Testing Results
Bibliographic record
Abstract
These data were collected as part of a voluntary initiative to create a White-Nose Syndrome Diagnostic Laboratory Network among laboratories participating in research and surveillance for Pseudogymonascus destructans (Pd) - the fungal pathogen causing White-Nose Syndrome in bats. Pd_qPCR_InterlaboratoryLODdata.xlsx is raw qPCR data from multiple laboratories running serial dilutions of Pd gBlock in known concentrations for the collectively used Muller (2013) Pd qPCR assay. Pd_qPCR_InterlaboratoryResults_LOD.xlsx contains the data output for each laboratory from running a generic LOD/LOQ calculator script. the generic LOD/LOQ calculator script is available at:https://github.com/cmerkes/qPCR_LOD_Calc. Pd_qPCR_InterlaboratoryPTResults_PanelData.xlsx contains the raw qPCR data from multiple laboratories running blinded samples spiked with known concentrations of Pd conidia. Each sample was extracted once and run in triplicate using the Muller (2013) assay. Pd_qPCR_InterlaboratoryPTResults_PanelResults contains the results of the blinded samples in each laboratory panel as both qPCR Ct values per replicate, and final overall sample result according to the WNS Case Definition. Pd_qPCR_InterlaboratoryPTResults_Standards.xlsx contains the results of the standard curves run by each laboratory in conjunction with the blinded sample panel.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.090 | 0.074 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".